User Similarly, Potter et al. (2023) related file combustion measurement with predictor variables of climate, fire weather indices, environmental variables and remotely sensed variables. He reported that the period where a larger fire year was witnessed and later season burning generally accounted for a higher mean combustion. Tree cover, elative humidity, Normalized Difference Infrared Index (NDII), and dNBR were the most important variable for aboveground combustion, while silt, slope, solar radiation, tree cover and sand were the drivers of combustion for belowground combustion. ChatGPT It seems like you’ve provided information from a study by Potter et al. (2023) that discusses the relationship between combustion measurements and various predictor variables, including climate, fire weather indices, environmental variables, and remotely sensed variables. The study highlights that larger fire years and later season burning generally lead to higher mean combustion.
According to the information you’ve shared, the study identifies specific variables that are important for aboveground and belowground combustion. For aboveground combustion, the key variables include tree cover, relative humidity, Normalized Difference Infrared Index (NDII), and dNBR (difference Normalized Burn Ratio). On the other hand, belowground combustion is influenced by variables such as silt, slope, solar radiation, tree cover, and sand.
If you have any specific questions or if there’s additional information you’re seeking, please let me know, and I’ll do my best to assist you!
User Error: LaTeX failed to compile introduction.tex. See https://yihui.org/tinytex/r/#debugging for debugging tips. See introduction.log for more info. In addition: Warning message: In system2(“fmtutil”, c(if (usermode) “–user” else “–sys”, “–all”), : error in running command Execution halted ChatGPT It looks like you’re encountering an error related to the compilation of a LaTeX file named “introduction.tex.” The error message suggests checking the “introduction.log” file for more detailed information about the issue.
Here are some general steps you can take to troubleshoot and resolve LaTeX compilation errors:
Check the Log File: Open the “introduction.log” file in a text editor to view the detailed error messages. This file often provides information about the specific error that occurred during the compilation.
Review LaTeX Code: Examine the LaTeX code in your “introduction.tex” file. Look for any syntax errors, missing packages, or other issues that might be causing the compilation error.
Package Installation: Ensure that all necessary LaTeX packages are installed. If you’re using specific packages in your LaTeX document, make sure they are available and properly installed.
Update LaTeX Distribution: Ensure that your LaTeX distribution is up to date. Outdated installations may lack necessary packages or have compatibility issues.
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Debugging Tips: Visit the URL mentioned in the error message (https://yihui.org/tinytex/r/#debugging) for additional debugging tips specific to the TinyTeX distribution.
User vs. System Mode: Pay attention to the warning about the “fmtutil” command. Depending on your setup, you might need to run LaTeX commands in user mode or system mode. Check your LaTeX distribution documentation for details.
Consult LaTeX Community: If you’re unable to resolve the issue, consider seeking help from the LaTeX community forums or mailing lists. LaTeX users often share their experiences and solutions to common problems.
Remember to save a backup of your LaTeX document before making any changes, and proceed step by step to identify and address the underlying issue causing the compilation error. If you can provide more details or share parts of your LaTeX code and log file, I may be able to offer more specific assistance.
User @article{brandt2009extent, title={The extent of the North American boreal zone}, author={Brandt, James P}, journal={Environmental Reviews}, volume={17}, number={NA}, pages={101–161}, year={2009} }
@article{french2011model, title={Model comparisons for estimating carbon emissions from North American wildland fire}, author={French, Nancy HF and De Groot, William J and Jenkins, Liza K and Rogers, Brendan M and Alvarado, Ernesto and Amiro, Brian and De Jong, Bernardus and Goetz, Scott and Hoy, Elizabeth and Hyer, Edward and others}, journal={Journal of Geophysical Research: Biogeosciences}, volume={116}, number={G4}, year={2011}, publisher={Wiley Online Library} }
@article{hookcarbon, title={Carbon Emissions from Biomass Burning}, author={Hook, Simon and French, Nancy and Giglio, Louis and Randerson, James and Roy, David and Schimel, David and Schroeder, Wilfred and Veraverbeke, Sander and Wooster, Martin} }
@article{ch2004landscape, title={Landscape assessment: ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio}, author={Ch, Key}, journal={FIREMON: Fire effects monitoring and inventory system}, year={2004}, publisher={USDA Forest Service, Rocky Mountain Research Station} }
@article{kurz2013carbon, title={Carbon in Canada’s boreal forest—a synthesis}, author={Kurz, Werner A and Shaw, CH and Boisvenue, Celine and Stinson, G and Metsaranta, J and Leckie, David and Dyk, Andrew and Smyth, Caroline and Neilson, ET}, journal={Environmental Reviews}, volume={21}, number={4}, pages={260–292}, year={2013}, publisher={NRC Research Press} }
@article{potter2023burned, title={Burned area and carbon emissions across northwestern boreal North America from 2001–2019}, author={Potter, Stefano and Cooperdock, Sol and Veraverbeke, Sander and Walker, Xanthe and Mack, Michelle C and Goetz, Scott J and Baltzer, Jennifer and Bourgeau-Chavez, Laura and Burrell, Arden and Dieleman, Catherine and others}, journal={Biogeosciences}, volume={20}, number={13}, pages={2785–2804}, year={2023}, publisher={Copernicus Publications G{"o}ttingen, Germany} }
@article{rogers2014quantifying, title={Quantifying fire-wide carbon emissions in interior Alaska using field measurements and Landsat imagery}, author={Rogers, Brendan M and Veraverbeke, S and Azzari, G and Czimczik, CI and Holden, SR and Mouteva, GO and Sedano, F and Treseder, KK and Randerson, JT}, journal={Journal of Geophysical Research: Biogeosciences}, volume={119}, number={8}, pages={1608–1629}, year={2014}, publisher={Wiley Online Library} }
@article{veraverbeke2015daily, title={Daily burned area and carbon emissions from boreal fires in Alaska}, author={Veraverbeke, S and Rogers, BM and Randerson, JT}, journal={Biogeosciences}, volume={12}, number={11}, pages={3579–3601}, year={2015}, publisher={Copernicus GmbH} } ChatGPT It seems like you’ve provided a list of bibliographic references in BibTeX format. If you have a specific question or if you need assistance with anything related to these references, feel free to let me know. Whether you need help citing them in a document, summarizing their findings, or any other related task, I’m here to assist you!
User How do i use this to provide an index citaion for potter et al., 2023 in my R markdown file ChatGPT To cite the “Potter et al., 2023” reference in your R Markdown file, you can use the citr package, which simplifies the citation process. Make sure you have the citr package installed. If you don’t have it installed, you can install it by running:
R Copy code install.packages(“citr”) Then, in your R Markdown document, you can use the following syntax to cite the reference:
markdown Copy code — title: “Your R Markdown Document” output: html_document —
Your document content goes here
Potter et al. (2023) investigated burned area and carbon emissions in northwestern boreal North America [@potter2023burned]. They found…
More content…
In the example above, @potter2023burned is the citation key associated with the “Potter et al., 2023” reference in your BibTeX file.
After adding the citation in your R Markdown document, you can knit it to produce the final output. When you knit the document, the citr package will automatically generate the correct citation based on the citation key you provided.
Remember to replace “Your R Markdown Document” with your actual document title and add your content accordingly. If you have more citations, you can separate them with commas like this: [@author1_year, @author2_year].
Additionally, make sure your BibTeX file is properly referenced in your R Markdown document. You can do this by adding the following YAML metadata at the beginning of your R Markdown file:
yaml Copy code — title: “Your R Markdown Document” output: html_document bibliography: your_bibliography_file.bib — Replace “your_bibliography_file.bib” with the actual filename of your BibTeX file.
After making these changes, you should be able to cite the references in your R Markdown document.
User Warning in install.packages : package ‘citr’ is not available for this version of R ChatGPT It appears that the citr package might not be available for your version of R or may not be present in the default CRAN repository. In such cases, you can use the remotes package to install it directly from GitHub.
Here are the steps to install the citr package from GitHub:
Install the remotes package (if you haven’t already): R Copy code install.packages(“remotes”) Use the remotes::install_github function to install the citr package: R Copy code remotes::install_github(“crsh/citr”) After these steps, you should have the citr package installed, and you can proceed with using it in your R Markdown document as described in the previous response.
If you encounter any issues during the installation process, please check the GitHub repository of the citr package for any additional instructions or updates.
User Do i ahve to do library (crsh/citr ChatGPT No, you don’t need to explicitly load the package using library(crsh/citr) after installing it using remotes::install_github(“crsh/citr”). The install_github function installs the package, but you still need to load it using library(citr) or library(“citr”) to make its functions available in your R session.
So, after the installation, you can add the following line to your R Markdown document to load the citr package:
r Copy code library(citr) This line should be placed at the beginning of your R Markdown document, typically after the YAML header and before you start using functions from the citr package to insert citations.
Remember that you only need to install and load the citr package if you plan to use it to manage citations in your R Markdown document. If you have an alternative method for handling citations or if you are manually managing citations, you may not need the citr package.
User how do i cite given ghe bibtet.bib document i uplaoded earlier is already save in my r markdown folder ChatGPT If your BibTeX file (e.g., references.bib) is in the same folder as your R Markdown document, you can include the following YAML metadata at the beginning of your R Markdown file:
yaml Copy code — title: “Your R Markdown Document” output: html_document bibliography: references.bib — Replace “Your R Markdown Document” with your actual document title, and ensure that the bibliography field points to the correct filename of your BibTeX file.
Once you’ve added this YAML metadata, you can then cite references in your document using the citation keys from your BibTeX file. For example:
markdown Copy code # Introduction
Some text introducing the topic and a citation [@author1_year].
Methods
Details about the methods employed in the study [@author2_year].
Results
Discussion of the results found by Potter et al. (2023) [@potter2023burned].
Conclusion
In conclusion, the findings of this study align with previous work [@author1_year; @author2_year].
References
Make sure to replace author1_year, author2_year, and potter2023burned with the actual citation keys used in your BibTeX file.
When you knit the R Markdown document, the citation keys will be replaced with the formatted citations, and the bibliography will be generated based on the entries in your BibTeX file.
User
output: pdf_document: null number_sections: yes geometry: left = 2.5cm, right = 2cm, top = 2cm, bottom = 2cm fontsize: 11pt html_document: df_print: paged word_document: default header-includes: -- ChatGPT It looks like you have a YAML header in your R Markdown document, specifying various options for the output format, including PDF, HTML, and Word. You’ve also included some LaTeX packages using the header-includes field.
One thing to note is that the bibliography field in the header-includes is not typically used in R Markdown. Instead, you should include it in the YAML header under the output field for the desired format (e.g., PDF). Also, you don’t need the \bibliography command in the header-includes section.
Here’s a modified version of your YAML header:
yaml Copy code — output: pdf_document: number_sections: yes latex_engine: pdflatex # Use pdflatex for PDF output toc: yes toc_depth: 3 citation_package: natbib html_document: df_print: paged word_document: default geometry: “left=2.5cm, right=2cm, top=2cm, bottom=2cm” fontsize: 11pt header-includes: -bibliography: bibliography.bib
In this modified version:
I moved the bibliography field under pdf_document, as it is specific to the PDF output. I added citation_package: natbib under pdf_document to specify the citation package for LaTeX. Make sure to replace bibliography.bib with the actual filename of your BibTeX file. Also, ensure that the YAML syntax is correctly maintained, especially the indentation.
User So how do i do an index citation now and mek sure the bibliography shows ChatGPT To create index citations and ensure that the bibliography is included, you can follow these steps in your R Markdown document:
Add a Citation in the Text: In your document, where you want to cite a reference, use the @citation_key syntax. Replace citation_key with the actual citation key from your BibTeX file. For example:
markdown Copy code According to Potter et al. (2023), the burned area and carbon emissions in northwestern boreal North America have been studied extensively [@potter2023burned]. Include Bibliography Section: At the end of your R Markdown document, add a section for the bibliography. The exact content may depend on the output format you’re using. For PDF output, you can use the following:
markdown Copy code # References
Make sure to replace bibliography.bib with the actual filename of your BibTeX file.
Knit the Document: Knit your R Markdown document. The citation keys in the text will be replaced with formatted citations, and the bibliography section will be generated at the end of the document.
Here’s a complete example:
markdown Copy code — output: pdf_document: number_sections: yes latex_engine: pdflatex toc: yes toc_depth: 3 citation_package: natbib html_document: df_print: paged word_document: default geometry: “left=2.5cm, right=2cm, top=2cm, bottom=2cm” fontsize: 11pt header-includes: -bibliography: bibliography.bib
Wildfire is the major standing disturbance in Canada.The area burned in Canada over the years has continued to progress with climate change as well as carbon released due to fire mediated disturbances for the year 2023. Here, statistical models will be established between field combustion and remotely sensed data from Landsat imagery as well as other environmental and climate variables obtained from the GEE datasets to estimate carbon emitted from the 2023 wildfires in order to derive a combustion map for 2023. The combustion datasets described here can be used across local and global scale for fire combustion science. The combustion datasets which will be described here [github.com/ChinyereRuth/Thesis] can be used for local to continental-scale applications of boreal fire science.
Keywords: Wildfire, carbon, spatial modelling, boreal forests
I am profoundly grateful to my dedicated supervisor, Dr. Alemu Gonsamo, whose unwavering guidance and support have been invaluable throughout my entire PhD journey. His expertise, encouragement, and commitment to excellence have significantly enriched my academic experience.
I extend my sincere appreciation to Dr. Anthonio Paeza for imparting his expertise and providing a comprehensive introduction to R and GitHub. His insightful teaching has equipped me with essential skills that have proven instrumental in my research endeavors.
1.0 Introduction
Worldwide, forests are vital carbon sinks and they store carbon belowground and aboveground. The boreal forest is the largest forest biome in the world and more than 20% of this zone is in Canada contributing to 8% of the world’s forests (Brandt, 2009). Canada’s boreal forests accumulate over 28 Pg of Carbon (Kurz et al., 2013) making them important global C reservoirs. A vast amount of Carbon has accumulated within this zone because of slow decomposition from cold temperatures and from their anoxic conditions (Kurz et al., 2013).
Although this zone has been known for a very long time be a C sinks, drier air temperatures attributed to climate change are now making them vulnerable to wildfire by enhancing drier fuels below and aboveground. Wildfire is currently the dominant disturbance impacting the boreal forest and accounting for vast amounts of C (Potter et al., 2023). Carbon loss is the release of particles, gasses and aerosols into the atmosphere. Wulder et al. (2020) estimated that about 1.79 Pg of biomass had been lost as a result of fire disturbance from 1985–2016. In addition to this, fire regimes are also changing, and these trends are projected to increase as climate change progresses Changes to fire regimes in Canada for an increase in area burned, fire intensity and fire severity leading to more carbon emission from fires aboveground and belowground. For example, over 17 Mha have burned just from the fires in 2023 alone in Canada. This is 5 times higher than the 2.37 Mha annual burn reported by Potter et al. (2023) in the past two decade. Changes in Canada’s fire regime (fire frequency, intensity, burned area, ire severity) have the ability to convert some of these forest biomass C stocks to carbon sources and this ecosystem might not return to their original biomass carbon storage.
To understand the impact of fires on Canada’s boreal forest where small fluctuations in temperature are already noted and projected to have significant effect on climate change (Phadnis and Carmichael, 2000), there is a critical need to estimate and understand the carbon loss from fire. This will not only help to improve carbon modelling and reporting but also provide mitigation measures for the areas where these trends are expected to be high.
There are two approaches to estimating carbon loss from fires. The first is the more traditional approach where the area burned, the fuel load (biomass consumed), the combustion completeness and the fraction of carbon are taken into account (French et al., 2011). The second approach is the remotely sensed method where statistical techniques are used to model combustion by establishing a relationship between field combustion and remotely sensed variables. For the more traditional approach of C emissions, a lot of uncertainties arises when accounting for fuel load and the combustion completeness as it differs from landscape to landscape (Hook et al.; Rogers et al., 2014)Hook et al., 2013). The latter is now used widely because of the availability of satellite imagery and the integration of field C measurements. Fuel load can be mapped with remote sensed observations and C loss information can be cover for a larger extent both spatial and temporally. Typically, remote sensing estimates of C loss rely on statistical approach between field measurements of C loss and absorption properties from either active or passive sensors. Several studies have found a significant relationship between remote sensing observation and carbon combustion across the boreal forests (Veraverbeke et al., 2015). The difference normalised burn ratio along other environmental spectral inputs, topographic and climate variables are derived to explain C loss. For example, Rogers et al. (2014) was able to estimate the amount of below and aboveground carbon loss from fire disturbance in Alaska using dNBR and field combustion. Veraverbeke et al. (2015) using geospatial data and Environmental variables reported that elevation, the day of burning, Burn severity (dNBR) and tree cover were the drivers of carbon emission from boreal fires in Alaska. Similarly, Potter et al. (2023) related file combustion measurement with predictor variables of climate, fire weather indices, environmental variables and remotely sensed variables. He reported that the period where a larger fire year was witnessed and later season burning generally accounted for a higher mean combustion. Tree cover, elative humidity, Normalized Difference Infrared Index (NDII), and dNBR were the most important variable for aboveground combustion, while silt, slope, solar radiation, tree cover and sand were the drivers of combustion for belowground combustion.
Carbon emission mapping such as our studies here will allow for improving C emission accounting, modelling and reporting and also more accuracte prediction of C losses which is a very key importance discussion within the united nations framework accounting.
Most of the research modelling carbon emission from disturbances such as wildfire only looked at Alaska and sometimes a combintaio of Alaska and western Canada. This research will be the first to provide belowground, soil and aboveground C emissions for the entire Canada Here, we are looking at the carbon emission rom fire from the entire Canada considering that this year, Canada had the worst fire seasons compared to other years. It is therefore important to understand the C that has been released from this disturbance and mitigation measures that can be carried out to help this ecosystem recovered from his disturbance. And also, carbon emission modelling of Canada will help in improving Canada C loss emission report and modelling and also to understand the drivers of C emission within Canada’s forests.
1.1 Research Aim & Objectives
The primary goal of this study is to estimate the amount of carbon loss from wildfire disturbance within Canada ’s forests. The specific objectives includes:
Estimating carbon loss by wildfire disturbance from Canada’s boreal forests.
Examine the factors responsible for combustion within the boreal forests.
2.0 Methods
2.1 Study area
The study area (Figure 1) is Canada’s forests region which comprises of boreal, great lakes, acadian, carolinian, subalpine, columbia, montane and coastal forest. Tree species within the zone are generally conifers such as black spruce, white spruce, Tamarack, balsam fir and jack pine while deciduous trees within the zone includes Aspen, balsam poplar and paper bitch. The climate of Canada’s forest varies but it is mainly characterized by cold winters and short summer (Brandt, 2009), although we are beginning to observed more warmer winter due to climate change. The mean temperature in Canada’s boreal forests ranges 10ºC- 20ºC and this varies from region to region (Zhang et al., 2019)
Canada’s forests regions (Natural resource Canada)
Wildfire is the major standing disturbance within Canada’s boreal forest and also other forest region with more than 2 Mha burn annually on average (Stocks et al., 2002) although this range varies from year to year.o determine areas that burned, a burned area map derived using a support vector machine learning algorithm will be used to identify area burned. To determine the areas that are primarily forests, a land cover map will be used to stratify the various land cover types. a land cover map will be used to stratify the areas of various landcover classes.
2.2 Field data
2.2.1 Field combustion area
To estimate the amount of C loss across Canada’s major disturbances, Field combustion measurements for the year 2014 and 2015 as shown in Figure 2 were acquired from a number of publications that carried out combustion measurement within Canada’s boreal forests and published online as a single database (Walker et al., 2020a).
This field measurements resulted in 456 plots and were used with other predictor variables to estimate C loss from Canada ’s boreal forest fires.
Field combustion area
2.2.2 Burned-area, harvest and insect disturbance mapping
Disturbnace map across the entire sty area will be drive using a random forest approach where we integrated different spectral indices, spectral bands and environmental variables. A threshold value established from literature and other filed collected disturbance site will be used on the final disturbance product to identify each disturbance types(fire, harvest and insect infestation) within Canada’s forests.
##2.3 Combustion models
2.3.1 Predictor variables
Field combustion measurements for aboveground and belowground C loss was obtained from Walker et al. (2020) and related to gridded environmental, fire severity and remotely sensed variables of combustion (Figure 3).
method to map C emissions from disturbance
The predictor variables of combustion used in this study was selected first by spanning across the literature and also discussing with experts and also further validated with a selection from random forests.
2.3.1.2 Climate variables
Climate variables used in this study was obtained from Climate NA. Climate NA provides monthly, seasonal and yearly gridded climate data. This data was used to generate climate impact of C combustion within Canada’s boreal forests.
2.3.1.3 Environmental variables
Aboveground combustion from fire depends on a lot of top tree influence. The environmental variables that we will use for this research includes elevation, Soil properties , slope, aspect, and pre-fire tree cover. The elevation data will be obtained from the Advanced Spaceborne and Thermal Emission and Reflectance Radiometer Global Digital Elevation Model (ASTER GDEM), and then slope and aspect will be derived in ArcGIS or R USING THE DEM.
Prefire tree cover plays a huge factor and influences below and aboveground combustion. Tree cover influences biomass fuel for burning and it is also a measure of tree stand. Studies have found out that tree cover correlates with C loss (Rogers et al., 2014). The pre-fire tree cover used in this study will be obtained rom Sexton et al et al. (2013) for the year201 and it will serve as the pre- fire tree cover for our combustion model
The differenced normalised burn ratio (dNBR) assess changes in fire impacted vegetation using the near and shortwave infrared reflectance (Key & Benson, 2004). dNBR will also be included as a predictor variable as studies have found out the dNBR correlates significantly with biomass loss. Spectral bands of Landsat 1- 9 wil be downloaded from the USGS and the NIR and SWIR band s will be used for deriving NBR (NIRSWIR)/(NIR + SWIR). dNBR will be computed as the difference between pre-fire and post fire NBR.
2.3.1.4 Remotely sensed variables
Remotely sensed variables used for this studies were all derived from Landsat 7 and 8. This include Landsat band 1 -9, the normalised differenced vegetation indices( NdVI), the topographic wetness index, Tasseled cap indices, and the Landsat dNBR
3.0 Results
3.1 Minimum Tempertaure
Across Canada in the month of May, the minimum temperatures (Figure 5) before the fire varied from -8 to 20 degrees Celsius. Notably, the provinces of the Northwest Territories, Alberta, British Columbia, Saskatchewan, and Ontario exhibited high minimum temperature values exceeding 10 degrees Celsius, while locations such as Yukon experienced lower minimum temperatures.
Minimum Air Temperature in Canada for the month of May, 2014
3.2 Mean Tempertaure
The mean temperature values, as depicted in Figure 5 for the month of May, closely paralleled the minimum temperature. Across lakes and rivers, the mean temperature values were lower. In contrast, regions such as the Northwest Territories, Alberta, British Columbia, Saskatchewan, and Ontario exhibited notably higher mean temperature values exceeding 20 degrees Celsius.
Mean Air Temperature in Canada for the month of May, 2014
3.3 Maximum Tempertaure
Minimum Air Temperature in Canada for the month of May, 2014
3.4 Prefire Tree Cover
According to Figure X, regions surrounding the boreal forests exhibited elevated prefire tree cover. Specifically, the provinces of Ontario and British Columbia demonstrated higher pre-fire tree cover compared to other provinces in Canada, as illustrated in the figure. In contrast, the prefire tree cover in the vicinity of the unmanaged boreal forest was notably lower.
Minimum Air Temperature in Canada for the month of May, 2014
3.5 Mean Area burned
When the fire ignited in May, the Slave Lake region in the provinces of the Northwest Territories was affected. The extent of the burned area varied across the provinces in Canada, as depicted in Figure X. The severity of the burn was more pronounced in the provinces of Alberta, the Northwest Territories, and Saskatchewan, where dNBR values higher than 0.5 were observed.
